Optimum inspection and maintenance policies for corroded structures using partially observable Markov decision processes and stochastic, physically based models

Abstract

Stochastic control methods have a history of implementation in risk management and life-cycle cost procedures for civil engineering structures. The synergy of stochastic control methods and Bayesian principles can result in Partially Observable Markov Decision Processes (POMDPs) that allow consideration of uncertainty within the entire domain of the model and expand available policy options compared to other state-of-the art methods. The superior attributes of POMDPs enable optimum decisions which are based on the belief space or otherwise only on the best knowledge that a decision-maker can have at each time. In this work the effort is mostly based in modeling and solving the problem of finding optimal policies for the maintenance and management of aging structures through a POMDP framework with large state spaces that can adequately and sufficiently describe real-life problems. In order to form the POMDP framework, stochastic, physically based models can be used and their connection to the control process is explained in detail. Specific examples of a corroded existing structure are presented, based on non-stationary POMDPs, for both infinite and finite horizon cases with 332 and 14,009 states respectively. Results from both cases are compared and discussed and the capabilities of the method become apparent.